The M13 API is designed around operations rather than loose model calls. It can return content together with usage, billing context, execution context and proof-oriented metadata.
Operational information, not legal advice.
Developer execution path
Builder entry
Start from SDK, API or reference app entry points instead of uncontrolled prompt work.
Execution path
Structure intake, execution, output and review as one governed workflow.
Proof layer
Keep run context, artifacts, usage and audit signals connected to the execution record.
Developer handoff
Move the pattern into an app, workflow or API-backed implementation.
Developer execution model
Builder entry, execution path, proof layer and developer handoff stay visible as one repeatable implementation model.
Platform proof
M13 API pages explain the platform path for developers who want AI applications with structured responses, execution context, usage and billing logic. The goal is not only to call a model, but to operate inside a controlled application layer.
Follow the builder path through the M13 API, inspect the AI execution ledger, or start building audited AI apps.
Execution structure
Builder path
This page describes platform and developer architecture for audited AI execution. It is not legal advice.
Related guides
Understand how auditable AI workflows connect steps, state, outputs and proof.
Understand how execution proof can support trust in serious AI workflows.
Use the EU AI Act Diagnostic as a learning path for building audited AI applications on m-pathy.
See how the EU AI Act Diagnostic works as a reference app for audited AI execution on m-pathy.
Official sources
Google documentation explaining how structured data helps search engines understand page content.
Google guidance for hreflang and localized URL variants, including self-referencing language alternates.
External references are included for source orientation. This page remains operational information and is not legal advice.